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The Interuniversity Consortium for Social and Political Research and the Indiana University Network Science Institute will be hosting two new short courses in July. There are still seats left for those interested in enrolling. Courses run 9-5 from Monday-Friday and registration is through the ICPSR summer program. http://www.icpsr.umich.edu/icpsrweb/content/sumprog/registration.html 


1. July 18-22: Egocentric Network Analysis (Brea Perry, with Erin Pullen) http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0213 

2. July 25-29: Network Analysis: Study Design and Methods (Bernice Pescosolido and Ann McCranie) http://www.icpsr.umich.edu/icpsrweb/sumprog/courses/0212 


Egocentric Network Analysis


Egocentric social network analysis (SNA) is a methodological tool used to understand the structure, function, and composition of network ties around an individual. Both sociocentric (i.e. whole) network analysis and egocentric network analysis share the basic assumption that behaviors, beliefs, attitudes, and values of individuals are shaped through contact and communication with others. However, these two methods are distinct in a number of important ways:


  1. Unbounded versus bounded networks. Sociocentric SNA collects data on ties between all members of a socially or geographically-bounded group and has limited inference beyond that group. Egocentric SNA assesses individuals' personal community networks across any number of social settings using name generators, and is therefore less limited in theoretical and substantive scope.
  2. Focus on individual rather than group outcomes. Sociocentric SNA often focuses on network structures of groups as predictors of group-level outcomes (e.g. concentration of power, resource distribution, information diffusion). In contrast, egocentric SNA is concerned with how people's patterns of interaction shape their individual-level outcomes (e.g. health, voting behavior, employment opportunities).
  3. Flexibility in data collection. Because sociocentric SNA must use as its sampling frame a census of a particular bounded group, data collection is very time-consuming, expensive, and targeted to a specific set of research questions. In contrast, because egocentric SNA uses individuals as cases, potential sampling frames and data collection strategies are virtually limitless. Egocentric data collection tools can easily be incorporated into large-scale or nationally-representative surveys being fielded for a variety of other purposes.

While no single course could cover the entire breadth of the field, we will examine the most fundamental methodological issues and practical concerns that arise in egocentric network research. This course requires no prior knowledge of egocentric SNA. We will begin with an introduction to the foundational concepts of egocentric SNA, highlighting linkages to theories commonly used in the social and health sciences (e.g. social capital). The rest of the course will cover methodological considerations and statistical techniques for egocentric SNA. In addition to covering data collection strategies (e.g. name generators, name interpreters), measures, and modeling in a lecture format, participants will learn to use Stata and E-NET software packages in daily lab sessions. These sessions will primarily focus on interactive use of Stata and E-NET in a computer lab, providing hands-on practice exercises using a range of substantive topics. E-NET is a free software package for egocentric network analysis and visualization created by the developers of UCI-NET.



Network Analysis: Study Design and Methods


Social network analysis (SNA) focuses on relationships between social entities. It is used widely in the social and behavioral sciences. The social network perspective, which will be taught in this workshop, has been developed over the last seventy years by researchers in psychology, sociology, political science, and anthropology. New interest in this field by physics, information science, social media studies, and biomedical fields has spiked in the past 15 years - this approach is often referred to as "network science." While this approach sometimes differs importantly in scale and substantive interest, it is often used to study the exact same problems as traditional SNA. This course will connect these two traditions in their terminology and specific methodological approaches.


This week-long workshop covers precisely those SNA concepts and tools, and has a special focus on how to design a network study and how to plan and execute data collection. It will present an introduction to various concepts, methods, and applications of social network analysis drawn from the social and behavioral sciences. The primary focus of these methods is the analysis of relational data measured on groups of social actors. Topics to be discussed include a basic introduction to SNA, graphs and matrices, basic network measures and visualization, reciprocity and transitivity, dyadic and triadic analysis, centrality, egocentric networks, two-mode networks (affiliations, bibliographic/scientometric analysis), cohesive subgroups, equivalences and blockmodeling, and a brief introduction to statistical modeling in network (ergm/p*/RSiena.)


Please note: The focus on statistical models (ergm/p*/Siena models) is limited and introductory in this course - those are the explicit focus of the other advanced courses in the ICPSR series. Also, this course focuses largely on "whole" or "complete" networks in which sociometric analysis is required. Egocentric analysis is not a primary focus of this course, but will be a topic of discussion and inclusion when appropriate with the rest of the course. 


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Ann McCranie ([log in to unmask])
Assistant Director of Research Administration
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